Computed tomographic colonography (CTC) was first suggested in the early 1980s as a potential method for mass screening of colorectal cancer, the second leading cause of cancer deaths in the US. CTC was first realized in the 1990s following the rapid progress in computed tomography (CT) and in digital computing. CTC is a minimally invasive method that involves the steps of CT imaging the whole abdomen and pelvis after cleansing and air insufflation of the colon. Since the first realization, several studies have been conducted assessing the performance of CTC, mostly based on a radiologist's visual examination of either two-dimensional (2-D) CT images or three-dimensional (3-D) virtual colonoscopic views, or both. Most efforts have been directed toward developing better visualization and navigation techniques, such as rendering, colon wall flattening, flight path planning algorithms, and user interface design. Recently some research has focused on developing computer-aided detection (CAD) methods for the identification of colonic polyps in 3-D CT data to improve the accuracy and efficiency of CTC. In these identification approaches, the 3-D geometrical features of polyps are extracted and used for their detection and identification. Mir et al. reviewed a set of methods proposed for shape description in CT images, e.g., moments, medial axis transforms, splines, curvature, Fourier descriptors, AR (Auto-Regressive) modeling, and statistical approaches (See A. H. Mir et al., “Description of shapes in CT images: The usefulness of time-series modeling techniques for identifying organs,” IEEE Eng. Med. Biol. Mag., vol. 18, pp. 79-84, January/February 1999). Summers et al. concluded that detection by shape analysis is feasible, especially for clinically important large polyps (See e.g. R. M. Summers et al., “Automated polyp detector for CT colonography: Feasibility study,” Radiology, vol. 216, no. 1, pp. 284-290, 2000; R. M. Summers et al., “Automated polyp detection at CT colonography: Feasibility assessment in a human population,” Radiology, vol. 219, no. 1, pp. 51-59, 2001). Paik et al. proposed to use a method based on overlapping surface normals to detect spherical surface patches along the colon wall that are likely to be parts of polyps (See e.g. D. S. Paik et al., “Computer-aided detection of polyps in CT colonography: Free response ROC evaluation of performance,” Radiology, vol. 217(SS), p. 370, 2000; D. S. Paik et al., “Detection of polyps in CT colonography: A comparison of a computer aided detection algorithm to 3-D visualization methods,” in Proc. 85th Scientific Sessions Radiological Society of North America, vol. 213(P). Chicago, Ill., 1999, p. 428). Yoshida et al. reported that geometric features extracted from small volumes of interest are effective in differentiating polyps from folds and feces (See H. Yoshida et al., “Detection of colonic polyps in CT colonography based on geometric features,” Radiology, vol. 217(SS), p. 582, 2000), as well as characterizing colon wall surface geometry (See H. Yoshida et al., “Three-dimensional computer-aided diagnosis scheme for detection of colonic polyps,” IEEE Trans. Med. Imag., vol. 20, pp. 1261-1274, December, 2001). Göktürk et al. fitted local spheres to the colon wall and based their detection on the existence of clusters of sphere centers (See S. B. Göktürk et al., “A graph method for the conservative detection of polyps in the colon,” in Proc. 2nd Int. Symp. Virtual Colonoscopy, Boston, Mass., 2000).
Most of these prior methods are rather sensitive (i.e. ability to detect positives), but need to be more specific (i.e. ability to detect negatives) for clinical applicability. The low specificity of some of the previously reported methods is generally due to the assumption that high curvature surface patches occur only on polyps. While it is true for instance that polyps have highly curved surfaces, so do some other structures, like haustral folds and retained stool. Radiologists reading these images use additional information to classify suspicious regions. For example, haustral folds are elongated structures, as opposed to polyps, which protrude locally from the colon wall. Stool may sometimes be identified by relatively inhomogeneous image intensity compared to polyps. However, if an automatic CAD method results in a low specificity manual examination of a (large) number of images corresponding to the CAD outputs is required to ensure proper detection. Such an examination is costly, time consuming and inefficient. Accordingly, there is a need to develop a method that would be capable of increasing specificity without sacrificing sensitivity. Such a method could also be used to enhance and classify outputs of a high-sensitivity low-specificity CAD method to eliminate false positives only.